Adaptive learning methods for selecting page components to include on dynamically generated pages

ABSTRACT

A subset of a set of components is selected for inclusion on a dynamically-generated page within a particular user context. The subset of components is selected based on scores associated with the components in the set. The score of a component is preferably determined based on measurements of user activity resulting from exposures of the components to users in the particular context.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/598,718, filed Jan. 16, 2015, which is a continuation of U.S.application Ser. No. 10/393,505, filed Mar. 19, 2003 (now U.S. Pat. No.8,965,998), which claims the benefit of U.S. Provisional Appl. No.60/366,343, filed Mar. 19, 2002. The disclosures of the aforesaidapplications are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the use of machine learning methods foridentifying web page content that is most likely to produce a desireduser action when incorporated into a dynamically-generated web page.

2. Description of the Related Art

Many web sites serve web pages that include one or more web pagecomponents (hereinafter referred to as “components”). A given componentmay, for example, contain content generated by a particular code moduleor service, and may occupy a particular area or section of a web page.Typically, the components contain links, buttons or other controls forallowing users to perform specific actions, such as adding a displayeditem to a shopping cart.

In selecting components to be incorporated into a web page, the web siteoperator typically wants to present the most effective set of componentsto the user. The effectiveness of presenting a component can be ameasure of whether a desired result is obtained from the user and/orwhether a desired action is performed by the user. The desired actionsor results can be any action or result an operator of a web site mightwant to obtain from a user. For example, desired actions for anadvertiser-supported on-line content provider might be, for example, theselection of a banner advertisement by a user or selection of ahypertext link to another page of the web site on which additionalbanner advertisements are displayed.

In accordance with existing techniques, in order to determine theeffectiveness of presenting a component to a user, web site operatorsmanually set up tests in which components are presented to users andactivity resulting from presenting the components is tracked. Thetracked activity can include any user activity of interest resultingfrom presenting the component to a user, such as a selection of ahypertext link included in the component, or an addition of a productdisplayed or represented by the component to a shopping cart or a wishlist. The tests are typically conducted in such a way that users are notaware that they are the subject of a test of the effectiveness of acomponent. Based upon analysis of the resulting activity, theeffectiveness of components can be determined. Determinations as towhich components to present to users can then be based upon thedetermined effectiveness of the tested components.

The use of manual tests in determining the effectiveness of componentshas several drawbacks. Conducting manual tests in order to determine theeffectiveness of components is very labor intensive. Due to this laborintensive nature, the number of tests and the level of detail of thetests are limited by available manpower. In addition, these teststypically also do not take into consideration differing tastes orpreferences among numerous particular types or classes of users. Manualtests also typically have a finite duration so that new tests must beconducted as new components are introduced and user trends change.Furthermore, once the results of a test are obtained, human interventionis typically required in order to propagate the results into a change incomponents that are displayed. The present invention seeks to addressthese deficiencies, among others.

SUMMARY

In accordance with one embodiment, an adaptive process uses collectedactivity data to select a subset of components from a set of componentsfor incorporation in a web page. The set of components, the details ofthe selection process, the activity data and the web page are preferablyassociated with a context representing a state of a user and/or a userbrowsing session.

Preferably, different components are repeatedly exposed to users anduser activity associated with each of the components is measured inassociation with a context. Activity data for a component accounts formeasured user actions that have resulted from previously exposing(presenting) the component to users within the context. The activitydata accumulated in association with the context is then used to selectcomponents that are likely to be most effective within the context(e.g., most likely to be of interest to the user or to most likelygenerate a desired response from the user) for inclusion in dynamicallygenerated web pages.

The context associated with a particular dynamically-generated web pagemay optionally reflect the browsing and/or purchase histories of usersof a web site, such that components presented on that web page over thesame time period vary from user to user. For example, if the currentvisitor to the web page is a frequent customer of the web site, acomponent may automatically be selected that has frequently produced adesirable result (e.g., an item purchase) when presented to otherfrequent customers on that web page. On the other hand, new customerswho access the same web page may be presented with a differentcomponent—one that has been particularly effective when presented to newcustomers.

Each component may, for example, be in the form of content generated bya particular code module or service. For instance, if the web page beingpopulated is a shopping cart page, one component may providepersonalized product recommendations based on the current contents ofthe user's shopping cart; another component may display a set ofproducts that are similar or related to a product just added to theshopping cart. Different modules can be invoked to provide differenttypes of components for inclusion on a web page. As will be understoodby one skilled in the art, modules that provide or generate componentscan be selected in a manner that has the same effect as selectingcomponents. Accordingly, for the sake of simplifying the presentdisclosure, the invention should be understood to apply to the selectionof modules as well as to the selection of components themselves.

In accordance with one embodiment, a system is configured to selectsubsets of components for inclusion in web pages. By repeatedlyselecting and including components in web pages served in response touser requests, the system also exposes components to users. Ascomponents are repeatedly exposed to users, the system detects andmeasures user activity resulting from component exposures.

In accordance with one embodiment, a method for selecting componentsfrom a set utilizes activity and exposure values collected inassociation with prior user activity in a context. For each component inthe context, the number of times the component is exposed to users iscounted. In addition, the number of user actions of interest associatedwith exposures of each component is counted and optionally weighted indetermining an activity value. A score is determined for each componentpreferably by dividing the activity value by the number of exposures. Asubset of components is selected from the set by selecting a desirednumber of components with the highest scores. Preferably, within eachcontext, the subsets of selected components are randomly varied to someextent such that most or all of the components in the set are exposed atleast a certain number of times.

In certain embodiments, multiple contexts are used. Preferably, acontext is identified through one or more attributes or valuesdescriptive of a user and/or a user browsing session. In accordance withone embodiment, the web server system maintains a set of state variablesfor each user and/or the user's browsing session. Prior to selecting asubset of components to present to the user, the system identifies acontext by matching current state values for the user to all of theapplicable attribute values for a context. Preferably, contextattributes are selected such that only one context will match any set ofstate variable values. Example state variables might include, forexample: (a) a variable configured to identify a category of a productthat was last added to the user's electronic shopping cart, (b) aboolean indication as to whether the user's electronic shopping cartcontains gift components, and (c) a web page identifier that identifiesthe web page or type of web page the user is browsing. A particularcontext might have attributes requiring that the values of thesevariables be respectively: (a) “GARDENING,” (b) “NO,” and (c) “SHOPPINGCART PAGE.” If the values of a user's state variables match thesecontext attributes, then the applicable context has been identified anda subset of components can be selected in accordance with the availableset of components, activity data, and process associated with thecontext.

The present invention may also be used to select items, such asproducts, to suggest to users within specific contexts that aredependent upon user-specific data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an adaptive process for selecting components inaccordance with one embodiment of the invention.

FIG. 1B illustrates an adaptive process for selecting components inaccordance with another embodiment of the invention.

FIG. 2 illustrates an example web page in accordance with oneembodiment.

FIG. 3 illustrates an example web page in accordance with anotherembodiment.

FIG. 4 illustrates a system for selecting subsets of components andserving web pages in accordance with one embodiment.

FIG. 5 illustrates a method for determining scores for components basedupon which components can be selected in accordance with one embodiment.

FIG. 6 illustrates a method for selecting components for inclusion on aweb page in accordance with one embodiment.

FIG. 7 illustrates an example set of state variables and two sets ofexample context variables for an on-line merchant.

FIG. 8 illustrates an alternative method for selecting subsets ofcomponents.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following description, reference is made to the accompanyingdrawings, which form a part hereof, and which show, by way ofillustration, specific embodiments or processes in which the inventionmay be practiced. Where possible, the same reference numbers are usedthroughout the drawings to refer to the same or like components. In someinstances, numerous specific details are set forth in order to provide athorough understanding of the present invention. The present invention,however, may be practiced without the specific details or with certainalternative equivalent devices, components, and methods to thosedescribed herein. In other instances, well-known devices, components,and methods have not been described in detail so as not to unnecessarilyobscure aspects of the present invention.

I. PREFACE A. Contexts

In accordance with one embodiment, a context defines an environment inassociation with which data is collected and in association with whichcomponents are selected for inclusion on web pages based upon thecollected data. A context can be identified or defined through one ormore attributes or values descriptive of, related to, and/oridentifying, for example, (a) a user, and/or (b) a state of a user'sbrowsing session. The attributes can be web browsing session specificvalues, such as the location of the current web page the user isrequesting or the locations of one or more previous web pages requestedby the user. The attributes can also be non-session specific values suchas the identity of a user, what the user has in an electronic shoppingcart, the past purchase history of the user, or how long the user hasbeen a customer of an on-line merchant.

B. Static and Dynamically Generated Web Pages

Web pages can be static or dynamically generated. Static web pages areauthored, created, or generated off-line and stored in a file system inadvance of a web page request. Upon receipt of a web page request, theweb page is read from the file system and served. Dynamically generatedweb pages are generated on-the-fly by a program, script or module inresponse to a web page request.

A dynamically generated web page is typically based upon a web pagetemplate or script, which is interpreted to generate the web page. Thetemplate includes code that specifies the structure of the web page andother aspects of the web page that do not change between requestedinstances of the dynamically generated page. The interpretation of theweb page template, however, can produce a web page with differentcontent or data, depending upon user or system variables at the time thepage is generated. Accordingly, dynamically generated web pagesfacilitate web page personalization, web page customization, and userinteractivity through web pages.

In accordance with a preferred embodiment, components are selected forinclusion in dynamically generated web pages. The components can beselected dynamically, in response to a request for the web page, or thecomponents can be selected off-line, in advance of a request for the webpage.

II. OVERVIEW A. Processes for Selecting Components

FIG. 1A illustrates an adaptive process for selecting components inaccordance with one embodiment of the invention. As illustrated, aselection process 102 operates on a set of components 104 using activitydata 105A associated with the components to produce a subset ofcomponents 106. The subset of components 106 is then incorporated into aweb page 108 that is served to a user. Activity data 105A for acomponent preferably includes data related to prior user actionsresulting from presenting the component to users. The selection process102 and the activity data 105A will be described in detail in the nextsection.

The set of components 104, the details of the selection process 102, theactivity data 105A, the subset of components 106, and the web page 108are preferably associated with a context 110A. The context 110A ispreferably identified through a set of attributes 112A. For any context110A, the set of components 104 associated with the context may bereferred to as a “context set.”

FIG. 1B illustrates an adaptive process for selecting components inaccordance with another embodiment of the invention. As illustrated, anordering process 122 operates on a list of components 124 using activitydata 105B associated with the components to produce a sublist ofcomponents 126. The sublist of components 126 is then incorporated intoa web page 128 that is served to a user. The ordering process 122 willbe described in detail in the next section in conjunction with theselection process 102.

The list of components 124, the details of the ordering process 122, theactivity data 105B, the sublist of components 126, and the web page 128are preferably associated with a context 110B. The context 110B ispreferably identified, characterized and/or defined by a set ofattributes 112B. For any context 110B, the list of components 124associated with the context may be referred to as a “context list.”

As will be understood by one skilled in the art, a list is an orderedset. Accordingly, for the sake of simplifying the present disclosure,references to sets should, as the context of the disclosure permits, beinterpreted as also applying to lists and vice versa.

B. Multiple Contexts

Many different contexts may be defined and used to populatedynamically-generated web pages of a given web site. For example, onecontext can be used to select components for a web page suggesting giftsfor men at a merchant web site and another context can be used to selectcomponents for a web page suggesting gifts for women at the site. Foreach context, subsets of components (each component configured to showone or more gifts) are selected from a context set of availablecomponents using a process 102 configured for the context. As will bediscussed in greater detail in the next section, the process 102preferably uses activity data 105 collected in association with thecontext in selecting the subsets.

In one embodiment, each of two or more contexts is configured to use thesame context set of components. The process 102 can be relied upon toselect subsets of components that will likely be of interest in eachcontext based upon prior user activity monitored in each context. Inanother embodiment, each of context can be configured to use a separatecontext list.

In certain embodiments, each of two or more contexts is configured touse the same process 102, such as the same set of calculations, forselecting subsets. In this case, different subsets of selectedcomponents can result from the use of different context sets and/or fromthe use of different data collected in association with the differentcontexts.

In certain embodiments, each of two or more contexts are configured touse the same activity data 105 for selecting subsets. In this case,different subsets of selected components can result from the use ofdifferent processes 102 in selecting subsets or from the use ofdifferent context sets 104 of components from which the subsets areselected.

In certain embodiments, only a single context might be used. As will beunderstood by one skilled in the art, when only a single context isused, no differentiation between multiple contexts is necessary andtherefore contexts need not be used at all.

III. Example Web Pages and Contexts A. Merchant Home Page

FIG. 2 illustrates an example home page 200 for a merchant web site. Theweb page 200 includes a first component 201 that shows the name of themerchant and provides a navigation bar that users can use to move aboutthe merchant's web site. Preferably this first component 201 is includedeach time the web page 200 is served.

The illustrated web page 200 includes three additional components 202,204, and 206 titled respectively:

-   -   “New in Books” (showing a single new book product)    -   “New & Future Releases” (showing a number of new and future        releases)    -   “Movers and Shakers” (showing products that have recently        increased in popularity, including the corresponding increases)

In accordance with one embodiment, the components 202, 204, and 206 areselected from a larger set of possible components. The larger set ofcomponents might include, for example, the additional components:

-   -   “Best Sellers”    -   “Discount Items”    -   “Recommendations” (can be tailored specifically for the user,        based upon the user profile)

In one embodiment, the web page 200 and the set of possible componentsare associated with a single context. The context can include a singleattribute, such as, for example, the value of the path of this web pagein the web site hosting the page:

-   -   web page identifier: /homepage.html

Alternatively, two or more contexts can be used for selecting componentsfor the web page 200 by using attributes that may depend upon theidentity of the user. For example, a first context can include thefollowing attributes:

-   -   web page identifier: /homepage.html    -   last purchase >1 year ago: Y        A second context can include the following attributes:    -   web page identifier: /homepage.html    -   last purchase >1 year ago: N        Any number of contexts can be configured to be used in        conjunction with a web page. Different contexts can be        associated with different user characteristics (e.g., new user,        frequent purchaser, electronics buff) or other variables, such        as, for example, time of day (e.g., morning, afternoon,        evening), or time of year (e.g., summer, winter, Christmas).

B. Electronic Shopping Cart Page

FIG. 3 illustrates another example web page 300 from a merchant website. On the left, the web page 300 shows an electronic shopping cart302 to which one selection 304 has been added. To the right of theshopping cart 302, the web page shows several components. The componentsof interest in this case are the three components of the web page311-313 titled:

-   -   “Customers who bought [the product added to the shopping cart]        also bought:”    -   “Quick Picks:”    -   “Top sellers in [the category of the product added to the        shopping cart]:”        In this case, each of the components 311-313 identifies        different types of products that the user might be interested in        purchasing.

Although only three components 311-313 are shown in the web page 300,these three components have preferably been selected from a setincluding several other components such as:

-   -   “Customers who shopped for [the product added to the shopping        cart] also shopped for:”    -   “Customers who shopped for [the product added to the shopping        cart] also shopped for:” but configured to display only products        in a similar product category (e.g., gardening)    -   “Customers who bought [the product added to the shopping cart]        also bought:” but configured to display only products in a        different product category (e.g., books)    -   “Recently viewed items:”    -   “Items on your wish list:”    -   “Items on [your friend's] wish list:”    -   “Accessories [for the product added to the shopping cart]:”    -   “Shopping Cart Based Recommendations:” where the recommendations        are determined based upon the contents of the shopping cart    -   “Purchase History Based Recommendations:” where the        recommendations are determined based upon the user's past        purchases    -   “Session-Based Recommendations:” where the recommendations are        determined based upon the products viewed by the user during the        current browsing session

Related to the above-referenced component titled “Customers who bought[the product added to the shopping cart] also bought:” is U.S. Pat. No.6,317,722, which discloses methods that can be used to select productsto recommend based upon the contents of a user's shopping cart. Relatedto the above-referenced components titled “Customers who shopped for . .. ” is U.S. patent application Ser. No. 09/821,826, filed Mar. 29, 2001,to Linden, Smith and Zada, which is assigned to the assignee of thepresent application and which is incorporated herein in its entirety byreference.

IV. SYSTEM AND METHODS

In this section, a system and methods for optimizing the selection ofcomponents for display on a dynamically-generated web page will bedescribed with reference to FIGS. 4, 5 and 6 in accordance with certainembodiments of the invention. FIG. 4 illustrates a system 400 forselecting subsets of components and serving web pages in accordance withone embodiment. Referring to FIG. 4, a web server system 400 includes aweb server 402 configured to respond to web page requests of users 404.In response to each page request, the web server 402 serves a web pageto the requesting user 404. Some or all of the web pages served by theserver 402 include subsets of components selected from larger sets ofcomponents.

FIG. 5 illustrates a method 500 for determining component scores, whichare used in selecting subsets, in accordance with one embodiment. Themethod 500 is preferably performed separately with respect to eachcontext of interest. Referring to FIG. 5, at a step 502, subsets of aset of components are repeatedly exposed (presented) to users. Acomponent is exposed, for example, by inclusion of the component on aweb page served to a user. Preferably, the subsets are varied to someextent such that most or all of the components in the set are exposed atleast a certain number of times. In response to each web page requestthat falls within the context of interest, the web server 402 servespages including subsets of the components in the set, preferably inaccordance with the method 600, described below. As a result, over time,the components in the set are repeatedly exposed to users.

At a step 504, for each component in the set, the number of times thecomponent has been exposed is counted or the amount the component hasbeen exposed is monitored. As will be discussed below, differenttechniques for counting or monitoring exposure can be used.

At a step 506, for each component in the set, user activity related toexposures of each component is monitored. The user activity can includeany activity performed by the user with respect to the exposed(displayed) component, such as, for example, traversal of a hypertextlink displayed by a component, the addition of a product displayed orrepresented by a component to a shopping cart, or a mouse-over eventdetected by an applet associated with the component. As will bediscussed below, different techniques for counting or monitoringactivity can be used.

Referring to FIG. 4, in order to monitor component exposure andactivity, the web server 402 preferably provides a page request log or apage request data stream to a data analysis module 406. The page requestlog or data stream preferably includes page request data, such ascomplete URLs, and/or user-related data associated with the users 404and their browsing sessions. Based upon the page request data, the dataanalysis module 406 preferably gathers component exposure and activitydata, associates the data with its proper context, and updates entriesin a database 408.

The database 408 preferably includes entries for one or more contexts110. For each context 110, data is preferably maintained for each ofseveral components 410, all of which make up a set or list. For eachcomponent 410, exposure data 412, activity data 105 and a componentscore 416 are preferably maintained.

The database 408 preferably also maintains component value data 418 foreach component. The value data can represent a value or benefit to anentity operating a web site that results from user activity related to acomponent. For example, for an on-line merchant, a value for a componentcan be representative of a margin, profit, or contribution associatedwith a user's purchase of a product identified by the component. In theillustrated system 400 the value data 418 for each component is notnecessarily associated with the context or contexts that include thecomponent. In an alternative embodiment, the component value data ismaintained for each component in association with a correspondingcontext. This value data 418, which will be discussed in greater detailbelow, can be used in determining a component's activity data 105 orscore 416.

At a step 508, for each component in the set, a component scoring module420 determines a score 416 based upon activity and exposure data for thecomponent. The score 416 can also be based upon value data 418 for thecomponent. The component scoring module 420 preferably stores the score416 in association with the component 410 and the context 110. As willbe discussed below, scores are preferably used in selecting componentsfrom sets to be included in subsets.

FIG. 6 illustrates a method 600 for responding to a request for a webpage by selecting components from a set in accordance with oneembodiment. At a step 602 the web server 402 receives a web page requestfrom a user 404. At a step 604, the web server 402 identifies anassociated context, which may be based upon the page request, the user'sweb browsing session and/or data related to the user. The method 600 ispreferably invoked in response to certain types of pages that areconfigured to utilize subsets of components.

At a step 606, in response to a request from the web server 402, acomponent selection module 422 selects a subset 106 of a set ofcomponents 104 associated with the context 110 for inclusion in the webpage 108. The subset of components 106 is preferably selected based uponthe scores 416 of the components in the context 110. In one embodiment,the subset is selected off-line, in advance of receiving the pagerequest. In this embodiment, the subsets of components for all contextscan be determined periodically and referenced as necessary. In anotherembodiment, the subset of components is selected dynamically, inresponse to the web page request.

At a step 608, in one embodiment, the component selection module 422optionally swaps into the subset 106 one or more components that havenot been selected. The swapping of unselected components into the subset106 provides an opportunity for activity to be measured for componentswith little or no prior exposure.

At a step 610, the web server 402 receives an identification of thesubset of components 106 from the component selection module 422 andincludes the subset of components is a web page 108. The web server 402then serves the web page in response to the user's request.

As will be understood by one skilled in the art some or all of theprocesses, methods, and modules described herein are performed orimplemented by programs or processes executed by one or more generalpurpose computers.

A. Monitoring Exposure

As discussed above with respect to the step 504, for each component in aset of components for a context of interest, the number of times thecomponent has been exposed is counted or the amount the component hasbeen exposed is monitored. Different techniques can be used to monitorexposure or count exposures. In one embodiment, for example, an absolutecount of a number of exposures starting from zero can be maintainedbeginning from some start date for each component.

In one embodiment, a number of exposures E_(new) is determined for eachday (or hour, week, month, etc.). The current exposure value E for a dayN is then determined based upon the daily exposure count E_(new), adecay factor d, which is preferably between 0 and 1, and an exposurevalue for the previous day E_(N-1) as shown in equation (1) below:E=E _(new) +dE _(N-1)  (1)In accordance with equation (1), data for each day is taken intoaccount, but the value of the effect of data for any particular day onthe value E decays over time. As exposure data is accumulated usingequation (1), the value of E_(N) will continue to grow untilE_(N-1)(1−d) becomes as large as E_(new) (at which point the scalingdown of E_(N-1) by the factor d is equal to the amount added byE_(new)). A typical weighting factor d is approximately 0.99. Thisprovides for a slow decay rate of the significance of historical values.If a faster decay rate is desired, a lower decay factor can be used. Forexample, if a component offers a seasonal product for sale or if thecomponent offers a product for which a new trend is emerging, a lowerdecay factor can be used for determining the component's exposure. Aswill be understood by one skilled in the art, the value of d can be setto 0, in which case past data is discarded altogether and E becomes themost recent daily value of E_(new). The value of d can be set to 1, inwhich case past data is weighted as heavily as current data and thegrowth of E becomes effectively unbounded.

As still another alternative, an exponentially moving average, alsousing a decay factor d, which is preferably between 0 and 1, can beused:E=E _(new)(1−d)+dE _(N-1)  (2)As the decay factor is decreased, the historical data decays out of thedetermination more quickly and new data is given more weight in thedetermination of E. Exponentially moving averages and theircharacteristics are well-known and will be familiar to one skilled inthe art.

B. Monitoring Activity

As discussed above with respect to step 506, for each component in theset, user activity related to exposures of each component is monitored.As will be understood by one skilled in the art, different kinds andtypes of user activity can be measured depending upon the application.For an information-related web site, traversals of hypertext links maybe the only activity of interest. For an on-line merchant, however,addition of products to a shopping cart, and/or the purchase of productsmay be actions of interest.

In certain embodiments, multiple types of actions are monitored andweighted in determining a measure of activity. For an on-line merchant,for example, the types of actions tracked might include (a) traversalsof component-related links, (b) addition of component-related product(s)to a wish list, and (c) addition of component-related product(s) to anelectronic shopping cart. Weighting factors W_(a), W_(b), and W_(c) canbe applied, respectively, to the numbers A_(a), A_(b), and A_(c) of eachof these types of actions depending upon the actual or estimated valueof those actions to the merchant to determine an overall activity valueA:A=W _(a) A _(a) +W _(b) A _(b) +W _(c) A _(c)  (3)For an on-line merchant, the values of W_(a), W_(b), and W_(c) might be1, 5, and 10, respectively.

In one embodiment, the activity value A is determined based upon numbersof related user actions counted starting from zero from some start datefor each component. Alternatively, decay factors or exponentially movingaverages can be used to determine current values for user actions basedupon historical data. Preferably, for each component, the same technique(and, if applicable, the same decay factor) is used for determining useractivity values and exposure values. As will be understood by oneskilled in the art, the decay and exponentially moving averagetechniques described above can be applied to either the numbers of thedifferent types of actions A_(a), A_(b), and A_(c) or to the overallactivity value A. In the case the decay or exponentially moving averagetechniques are applied to the numbers of the different types of actions,current values for the individual types of actions A_(a), A_(b), andA_(c) are preferably maintained in the database. In the case the decayor exponentially moving average techniques are applied to the overallactivity value, a daily activity value can be calculated and combinedwith a historical activity value to determine a new activity value eachday. In this case, only the activity value A needs to be maintained inthe database.

In certain embodiments, an activity value can also be based uponcomponent value data 418. As discussed above, component value data canrepresent a value or benefit to an entity operating a web site thatresults from user activity related to a component. For example, for anon-line merchant, a value for a component can be representative of amargin, profit, or contribution associated with a user's purchase of aproduct identified by the component. The value data can be taken intoaccount, for example, by incorporating a value factor V into theactivity calculation:A=W _(b) VA _(b) +W _(c) VA _(c)  (4)As used in equation (4), the value factor V can be a relative valuerepresentative of expected, actual, or measured margin or profit to anon-line merchant associated with certain actions, such as (b) additionof component-related product(s) to a wish list and (c) addition ofcomponent-related product(s) to an electronic shopping cart.

As will be understood by one skilled in the art, equations (3) and (4)are intended only as examples. In other embodiments, differentcalculations can be used to suit the purposes of the particularapplication. For example, by combining aspects of equations (3) and (4),an activity calculation can be configured to take value factors intoaccount with respect to some actions but not others.

In certain embodiments, two or more separate activity values can becalculated using different equations and each of the activity values canbe maintained in the database 408. As will be discussed below, multipleactivity values can be weighted and combined in a score determination.

C. Score Determination

As discussed above with respect to step 508, for each component in aset, a score is determined based upon activity and exposure data for thecomponent. In one embodiment, the score S of a component is determinedby dividing the activity value A by the exposure value E:

$\begin{matrix}{S = \frac{A}{E}} & (5)\end{matrix}$

In certain embodiments, once all of the scores of the components in acontext set are determined, the scores are normalized by dividing eachscore by the maximum of all of the scores. For example, the score S of acomponent is divided by a maximum S_(max) of all the scores in thecontext set to produce a normalized score S_(normalized) for thecomponent.

$\begin{matrix}{S_{normalized} = \frac{S}{S_{maz}}} & (6)\end{matrix}$

In certain embodiments, multiple scores are determined for eachcomponent using multiple activity values. For example, a first scoreS_(A) for a component can be based upon a first activity value A_(A) anda second score S_(B) for the component can be based on a second activityvalue A_(B) using equation (5) above. The multiple scores can benormalized using equation (6) and combined using a weighting factor W′to determine an overall score for each component:S=W′S _(A-normalized)+(1−W)S _(B-normalized)  (7)

In one embodiment, equation (7) is used by an on-line merchant with aweighting factor W′ of 0.6. In this embodiment, S_(A-normalized) is anormalized score based on user actions (a), (b), and (c) referred toabove using a form of equation (3):A _(A) =A _(a)+5A _(b)+10A _(c)  (3A)S_(B-normalized) is a normalized score based only on user actions (b)and (c) referred to above using a form of equation (4):A _(B)=5VA _(b)+10VA _(c)  (4B)

D. Context Identification

FIG. 7 illustrates an example set of state variables 710 and two sets ofexample context attributes 720A-B for an on-line merchant. The statevariables 710 contain values related to a user and/or the user'sbrowsing session. The attributes 720A-B define and identify theirrespective contexts.

The state variables 710, which can be used to store data about the userand/or the user's browsing session are preferably maintained and updatedby the web server system 400 for each user. In one embodiment, the statevariables 710 are maintained in a user profile (set of user-relateddata) for each user. Each of a set of example state variables in FIG. 7is listed by a descriptive name, followed by a range of possible valuesin parentheses. Some of the state variables are web-browsing sessionspecific, such as the “web page identifier” which is an identifier ofthe current web page that is being browsed by the user. Other variables,such as the “last purchase >1 year ago?” variable, is not specific tothe session, but rather is determined based upon past actions of theuser, possibly during previous web browsing sessions.

The context attributes 720A and 720B specify values for the three statevariables: “category of product last added to cart,” “cart containsgifts?,” and “web page identifier.” The value of the attribute “cartcontains gifts?,” however is different for the two contexts A and B andthis distinction differentiates the two contexts. Other contexts caninclude attributes that correspond to other state variables or sets ofstate variables.

As discussed above with reference to step 604, the web server 402identifies a context, which may be based upon the page request, theuser's web browsing session and/or data related to the user. Inaccordance with one embodiment, a context is identified by matchingcurrent state values for a web browsing session to all of the applicableattribute values for a context. Preferably, context attributes areselected such that only one context will match any set of state variablevalues.

In one embodiment, values of state variables are coded into bit fieldsin a binary number and the resulting number defines and identifies thecontext. For example, for an on-line merchant, attributes can be encodedas bit field elements as follows:

-   -   1=recognized customer    -   2=last purchase more than 1 year ago    -   4=purchased more than 10 components    -   8=shopping cart contains more than $100    -   16=shopping cart contains between $50 and $100    -   32=order contains gift components        A customer that has only been a customer for three months, has        purchased twelve components in that time, and has a $60 book        selected as a gift in his shopping cart will be in the context        represented by the number 53. By encoding attributes as bit        fields, the context can be easily be determined on the fly by        testing each of the aforementioned conditions and adding        together the resulting values.

Contexts can be made specific or general depending upon which, how many,and how attributes are specified. A general context, for example, canhave a single attribute, such as a web page identifier of a web page ofrecommended gifts at a merchant web site. A specific context, forexample, can take into account more specific attributes, such as, forexample, whether the user's shopping cart contains gifts and thecategory of the product last added to the shopping cart.

E. Selecting a Subset or Sublist

As discussed above with reference to step 606, in response to a requestfrom the web server 402, the component selection module 422 selects asubset 106 of a set of components 104 for inclusion in the web page 108.The subset of components 106 is preferably selected based upon thescores 416 of the components in the context 110.

Preferably, the method 500 has been performed for the context identifiedin step 604 such that each of the components in the context set has ascore. In one embodiment, a subset of N components is selected byselecting the N components with the highest scores in the context set.Alternatively, in the case a list is used, the context list is orderedbased upon the scores determined through the method 500. Then, Ncontiguous components, preferably the first N in the list, are selectedas the sublist.

The value of N or the size of the set is preferably determined by theweb server 402 based upon the specification, structure or template ofthe web page into which the selected subset of components is to beincorporated.

F. Swapping Unselected Components into the Subset

As discussed above with reference to step 608, the component selectionmodule 422 optionally swaps into the subset 106 one or more componentsthat have not been selected. The swapping of unselected components intothe subset 106 provides an opportunity for activity to be measured forcomponents with little or no prior exposure.

Preferably, in a certain percentage or proportion of instances in whichthe method 600 is performed for each context, one or more componentsthat were initially selected in step 606 are swapped out of the subsetand replaced with components that have not been selected. The selectionof the replacement components is preferably random, so as to ensure thatover time all of the components in the context set obtain some exposure.Preferably, components are swapped in about 10% to 20% of all cases.

V. Additional Factors A. Determining Initial Scores

When a new component is introduced to a context set, the component willlikely have no exposure data or the exposure value might be 0. If theexposure value is 0, a calculation of the score using equation (5)results in a division by 0 and cannot be used. In these situations, adefault score, such as the average of all scores, can be used for thecomponent. An initial score can be chosen automatically or manually. Inone embodiment, the initial score can be automatically based onobjective criteria, such as current sales rank of a product. In oneembodiment, the initial score can be manually chosen based on subjectivecriteria such as perceived value of the component or expectation ofperformance.

B. The Root Context

In one embodiment, the database maintains a separate context thatincludes an aggregation of all of the exposure and activity data for allcomponents in all contexts. This separate context will be referred to asthe root context.

In the case a new component is introduced, it may take some time beforethe component obtains enough exposure such that a statisticallysignificant amount of exposure and activity data is accumulated in eachcontext in which the component is included. Accordingly, until a certainthreshold number of exposures is accumulate in a context, the exposureand activity data contained in the root context can be used to calculatea score for the component in other contexts. In a preferred embodiment,the root context is relied upon until a component obtains at least 25exposures in a context of interest.

When a new context is created, a complete context set of components iseffectively introduced and each of the new components can be treated asa new component. Accordingly, in one embodiment, scores for the newcomponents can be based on the root context until each of the componentsaccumulates a statistically significant amount of data within the newcontext.

In one embodiment, the root context can be used to track performance ofcomponents or content in the aggregate. In this aspect, performance inthe root context can be used when evaluating whether to remove a poorperformer from some or all contexts.

C. Accounting for Low Exposure Numbers

When a new component is introduced to a set of context sets, it may takesome time until a statistically significant number of exposures isaccumulated in even the root context.

In one embodiment, in order to enable activity data to accumulate morequickly when new components are introduced, the decay factor d, inequation (1) can be set to 1 until a statistically significant number ofexposures, such as 25, are obtained.

In certain embodiments, scores based upon larger number of exposures arefavored as statistically more reliable. In order to favor these scores,activity values can be adjusted by the number of exposures E inaccordance with the following equation before scores are determined:

$\begin{matrix}{A_{adjusted} = {A\left( {1 - \frac{1}{\sqrt{E}}} \right)}} & (8)\end{matrix}$Preferably, the minimum number of exposures is 25 and so the mostsignificant adjustment is a multiplication by 0.8.

A more general form of equation (8) is:

$\begin{matrix}{A_{adjusted} = {A\left( {1 - \frac{K}{\sqrt{E}}} \right)}} & \left( {8A} \right)\end{matrix}$where K is a non-negative constant, such that K≦√{square root over (E)}.As long as the score S is not computed until E is above some threshold,then K can be larger than 1. K can always be smaller than 1. In equation(8A), when K is large, the penalty applied to components with lowexposure data is larger. When K is small, there is less penalty and atthe extreme of 0, there is no penalty applied.

D. Accounting for Area Occupied by Components on Web Pages

Referring to FIG. 3, the first component on the web page occupiesapproximately twice as much area on the web page as the subsequent twocomponents. In one embodiment, the area a component occupies on a webpage is accounted for by dividing the activity value of the component bythe relative area occupied by the component on the web page:

$\begin{matrix}{A_{siae} = \frac{A}{area}} & (9)\end{matrix}$The areas can be specified in relative terms such that small integerareas are used. For example, the area of the first component in FIG. 3can be selected to be 2, while the areas of the second and thirdcomponents can be selected to be 1. In this embodiment, scores ofcomponents are representative of activity per exposure per unit area ona web page.

E. Accounting for Placement of a Component on a Web Page

Oftentimes, web pages are larger in size than can be displayed in a webbrowser window without scrolling. Experience has shown that componentsthat appear on a web page without requiring a user to scroll down tendto be selected more often that components shown lower down, that requirescrolling to be seen. Also, components near the very bottom of the pagetend to be selected more often than components near the middle of thepage length.

In one embodiment, the value for counting each instance of a component'sexposure is divided by a factor that takes into account placement on aweb page. For example, exposure of a component near the top of a longweb page can be counted as 1.0 exposures, a component near the bottom ofa web page can be counted as 0.7 exposures, and a component placed nearthe middle of a web page can be counted as 0.5 exposures.

In accordance with one embodiment, locations on a web page are scored byplacing the same component in different locations on the web page andaccumulating activity data related to the different locations. Thescores for the different locations, in turn, can be used in determiningthe values of the locations for the purpose of counting exposures.

F. Alternatives to Swapping Components into a Subset

As discussed above, the step 606 is preferably optionally performed in aportion of all instances in which the method 600 is performed, such as10% or 20%. The step 606 is preferably performed to enable componentsthat otherwise would not be exposed at all, to obtain some exposurebased upon which activity data can be collected.

FIG. 8 illustrates an alternative method 800 for selecting subsets ofcomponents. The steps 602, 604, 606, and 610 are preferably the samesteps described above with reference to the method 600. In the method800, however, at a step 805 a determination is made as to whether theresponse to the user's request is to be one of a test group of web pagesconfigured to provide exposure to components that would otherwise notreceive exposure. Preferably about 10% to 20% of all web page requestare randomly selected to be in the test group. Of those requests thatare not chosen to be in the test group, control flows to the step 606for selection of a subset, preferably in accordance with the descriptionabove. Control then flows to the step 610 in which the subset selectedin step 606 is included in a web page.

In the case a user's request is chosen to be in a test group, controlflows from the step 805 to a step 808. At the step 808, a subset ofcomponents is randomly selected from the context set. Control then flowsto the step 610 in which the randomly selected set is incorporated intothe served web page.

In accordance with one embodiment, in the step 808 the method forrandomly selecting the subset is configured to take into account eachcomponent's score. The random selection is preferably configured suchthat the likelihood of a component's being randomly selected isproportional to the component's score. In one embodiment, the step 808is performed as follows: first, each of a range of numbers is associatedwith a component of a context set such that the number of numbersassociated with each component is proportional to the component's score;second, a random number is selected within the range and the componentassociated with the random number is selected; third, the selectedcomponent is placed in a list or a set of selected components andremoved from the set of selectable components; and fourth, the first,second, and third steps are repeated until the desired number ofcomponents are selected. As will be understood by one skilled in theart, other techniques are known and can be used for weighting the randomselection for components.

In one embodiment, a web browsing session ID of the user requesting theweb page is used as a seed for generating the random numbers in the step808. In this embodiment, therefore, the subset of components isgenerated dynamically, on-the-fly, in response to the user's request inthe step 808. In other embodiments, the randomly selected subsets can begenerated off-line, in advance of the user's request based upon a randomseed other than the requesting user's web browsing session ID.

G. Selecting Ordering of Components Only

In certain embodiments, a set of components need not be selected and theinvention can be used merely to select the order or placement ofcomponents on a web page. It may be the case that the set of componentsto be displayed on a web page has been determined in advance. In theseembodiments, activity data can be collected for each of the componentsand placement of the components on a web page can be based uponcomponent scores.

VI. DYNAMIC SELECTION OF ITEMS

The foregoing methods are also applicable to the selection of items topresent within dynamically-generated web pages. The items may, forexample, be static product names or descriptions read from a productsdatabase.

For example, the invention may be used to vary a set of “relatedproducts” displayed on a product detail page of an electronic catalogbased on information known about specific users. To do this, a masterset of related products may be initially defined for a particularproduct detail page, and one or more contexts may be defined for thatpage. The context or contexts may, for example, take into considerationthe number or dollar amount of prior purchases made by the users withinthe product category to which the detail page corresponds. Over time,context-specific activity data may be collected and analyzed for eachrelated product within the master set using the same methods asdescribed above. The results of this analysis may in turn be used toselect, on a user-specific basis, a subset of the related products topresent on the product detail page.

VII. CONCLUSION

Although the invention has been described in terms of certainembodiments, other embodiments that will be apparent to those ofordinary skill in the art, including embodiments which do not provideall of the features and advantages set forth herein, are also within thescope of this invention. Accordingly, the scope of the invention isdefined by the claims that follow. In the claims, the term “based upon”shall include situations in which a factor is taken into accountdirectly and/or indirectly, and possibly in conjunction with otherfactors, in producing a result or effect. In method claims, referencecharacters are used for convenience of description only, and do notindicate a particular order for performing a method.

What is claimed is:
 1. A computer process for adaptively selectingcomponents to include on dynamically generated electronic pages, theprocess comprising: collecting exposure data for exposure events inwhich a component is included on dynamically generated electronic pagesserved to requesting devices, the exposure data including positionalvalues reflecting display positions at which the component is includedon the dynamically generated pages; collecting activity data reflectiveof user interactions with the component as included on the dynamicallygenerated electronic pages, the activity data based on monitoredinteractions of a plurality of users; calculating a score for thecomponent based on at least (1) the collected exposure data, includingthe positional values, and (2) the collected activity data, the scorerepresenting an effectiveness of the component; receiving a page requestfrom a user device; and generating an electronic page to provide to theuser device in response to the page request, wherein generating theelectronic page comprises determining, based at least partly on thescore, whether to include the component on the electronic page; saidprocess performed by a computing system under control of executableprogram instructions.
 2. The process of claim 1, wherein the positionalvalues are selected such that more exposure credit is given to exposureevents in which the component is positioned near the top of a page thanto exposure events in which the component is positioned near the bottomof a page.
 3. The process of claim 1, wherein the process comprises:collecting the exposure data and activity data separately for each of aplurality of defined contexts in which the component is capable of beingdisplayed; and calculating a respective context-specific score for eachof the plurality of contexts based on the exposure and activity datacorresponding to the respective context.
 4. The process of claim 3,wherein generating the electronic page comprises determining a contextof the page request, said context being dependent upon a user attributeassociated with the user device, and looking up the context-specificscore corresponding to the determined context.
 5. The process of claim4, wherein the context is additionally dependent upon a past browsinghistory of a user of the user device.
 6. The process of claim 1, whereinthe component is a dynamically generated component that displays contentthat depends upon a context in which the component is displayed.
 7. Theprocess of claim 1, wherein generating the electronic page comprisesusing a random selection algorithm in which a likelihood that the pagecomponent is selected for inclusion on the electronic page is dependenton the score.
 8. A system for selecting components to include ondynamically generated pages, the system comprising: a plurality of pagecomponents represented in computer storage; a data repository ofexposure data reflective of exposure events in which the page componentsare selected for inclusion on dynamically generated pages, the exposuredata including positional values reflective of positions at which thecomponents are included on the dynamically generated pages; a datarepository of activity data reflective of user interactions with thepage components as included on the pages, the activity data based onmonitored interactions of a plurality of users; and a computer systemprogrammed with executable instructions to calculate, for each pagecomponent, a score representing an effectiveness of the respective pagecomponent, the scores based on the exposure data, including thepositional values, and based additionally on the activity data.
 9. Thesystem of claim 8, further comprising a component selection module thatuses the scores to dynamically select the page components to include onpages in response to requests from user devices.
 10. The system of claim9, wherein the component selection module uses a random selectionalgorithm in which a likelihood that a page component will be selectedfor inclusion on a requested page depends on the score corresponding tothe component.
 11. The system of claim 8, wherein the exposure data andactivity data are stored separately for each of a plurality of definedcontexts in which page components can be displayed, and wherein thecomputer system is programmed to calculate, for a page component, aplurality of context-specific scores, each of which represents aneffectiveness of the page component within a respective context.
 12. Thesystem of claim 8, further comprising a plurality of context definitionsstored in computer storage, each context definition defining a differentrespective one of the plurality of contexts, wherein at least some ofthe context definitions include user attribute values.
 13. The system ofclaim 8, wherein at least one of the page components displays contentthat is dependent upon a context in which the component is displayed.14. A computer-implemented method of selecting components to include ondynamically generated pages, the method comprising: receiving, from auser computing device of a user, a request for a dynamically generatedpage; mapping the request to a context defined in computer storage, saidcontext being one of a plurality of defined contexts in which the pageis capable of being requested; for each of a plurality of candidatecomponents, determining a respective context-specific score thatreflects an effectiveness of the candidate component in the context,wherein each score is dependent upon responses of prior users topresentation of the respective component in the context; selecting,based at least in part on the scores, a subset of said plurality ofcandidate components to present to the user on the page; and dynamicallygenerating the page in response to the request, wherein dynamicallygenerating the page comprises incorporating the selected subset ofcomponents into the page; the method performed programmatically by acomputing system that comprises a server.
 15. The method of claim 14,wherein mapping the request to a context comprises selecting the contextbased at least partly on an attribute of the user.
 16. The method ofclaim 15, wherein mapping the request to a context comprises selectingthe context based additionally on a state of a browsing session of theuser.
 17. The method of claim 14, wherein the scores dependent upondisplay positions at which the components were presented to the priorusers on pages.
 18. The method of claim 14, wherein the componentdisplays dynamically selected content that depends on the context, andthe method further comprises dynamically selecting the content inresponse to the request.
 19. The method of claim 14, wherein the subsetof candidate components is selected based additionally on arandomization function.